Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.

Journal: The British journal of radiology
Published Date: Sep 3, 2023

Abstract

Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.

Authors

  • Lise Wei
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Dipesh Niraula
    Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States.
  • Evan D H Gates
    Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, Texas, USA.
  • Jie Fu
    David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, 90095, CA, USA.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Matthew J Nyflot
    Department of Radiation Oncology, University of Washington, Seattle, WA, USA.
  • Stephen R Bowen
    Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington, USA.
  • Issam M El Naqa
    Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States.
  • Sunan Cui
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

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